Search Results for author:
Found 10 papers, 6 papers with code
RESTOR: Knowledge Recovery through Machine Unlearning
Large language models trained on web-scale corpora can memorize undesirable datapoints such as incorrect facts, copyrighted content or sensitive data.
Understanding and Mitigating Compositional Issues in Text-to-Image Generative Models
We also show that re-weighting the erroneous attention contributions in CLIP can also lead to improved compositional performances, however these improvements are often less significant than those achieved by solely learning a linear projection head, highlighting erroneous attentions to be only a minor error source.
On Mechanistic Knowledge Localization in Text-to-Image Generative Models
To address this issue, we introduce the concept of Mechanistic Localization in text-to-image models, where knowledge about various visual attributes (e. g., "style", "objects", "facts") can be mechanistically localized to a small fraction of layers in the UNet, thus facilitating efficient model editing.
Online Advertisements with LLMs: Opportunities and Challenges
This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems.
Regret Analysis of Repeated Delegated Choice
To mitigate this behavior, the principal announces an eligible set which screens out a certain set of solutions.
PRIME: Prioritizing Interpretability in Failure Mode Extraction
To improve on these shortcomings, we propose a novel approach that prioritizes interpretability in this problem: we start by obtaining human-understandable concepts (tags) of images in the dataset and then analyze the model's behavior based on the presence or absence of combinations of these tags.
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
Moreover, we show that watermarking methods are vulnerable to spoofing attacks where the attacker aims to have real images identified as watermarked ones, damaging the reputation of the developers.
Text-To-Concept (and Back) via Cross-Model Alignment
We observe that the mapping between an image's representation in one model to its representation in another can be learned surprisingly well with just a linear layer, even across diverse models.
Run-Off Election: Improved Provable Defense against Data Poisoning Attacks
Based on this approach, we propose DPA+ROE and FA+ROE defense methods based on Deep Partition Aggregation (DPA) and Finite Aggregation (FA) approaches from prior work.
